A feasibility study proposal of the predictive model to enable the prediction of population susceptibility to COVID-19 by analysis of vaccine utilization for advising deployment of a booster dose

Abstract

With the present highly infectious dominant SARS-CoV-2 strain of B1.1.529 or Omicron spreading around the globe, there is concern that the COVID-19 pandemic will not end soon and that it will be a race against time until a more contagious and virulent variant emerges. One of the most promising approaches for preventing virus propagation is to maintain continuous high vaccination efficacy among the population, thereby strengthening the population protective effect and preventing the majority of infection in the vaccinated population, as is known to occur with the Omicron variant frequently. Countries must structure vaccination programs in accordance with their populations' susceptibility to infection, optimizing vaccination efforts by delivering vaccines progressively enough to protect the majority of the population. We present a feasibility study proposal for maintaining optimal continuous vaccination by assessing the susceptible population, the decline of vaccine efficacy in the population, and advising booster dosage deployment to maintain the population's protective efficacy through the use of a predictive model. Numerous studies have been conducted in the direction of analyzing vaccine utilization; however, very little study has been conducted to substantiate the optimal deployment of booster dosage vaccination with the help of a predictive model based on machine learning algorithms.